In [1]} a reset policy is considered together with the reinforcement learning policy to reset the environment prior to an expensive-to-reset situation. For example, an autonomous car would crash in high speed and resetting the environment would be very costly. After the crash state, the environment should be reset manually. Based on this example, a reset policy is necessary for some tasks to decrease the number of manual resets. In [1]}, off-policy actor critic method is used to learn policies, where the \(Q\)  values of the main policy and the reset policy are jointly learned. The reset policy takes over selecting an action to abort the episode if its Q value for a particular action taken by forward policy is lower than a threshold. The safe actions are the reversible sequence of actions where the agent can always undo them.
